The Cumulative Perioperative Model: Predicting 30-Day Mortality in Abdominal Surgery Cancer Patients.
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ABSTRACT: Objectives
1) To develop a cumulative perioperative model (CPM) using the hospital clinical course of abdominal surgery cancer patients that predicts 30 and 90-day mortality risk; 2) To compare the predictive ability of this model to ten existing other models.Materials and methods
We constructed a multivariate logistic regression model of 30 (90)-day mortality, which occurred in 106 (290) of the cases, using 13,877 major abdominal surgical cases performed at the University of Texas MD Anderson Cancer Center from January 2007 to March 2014. The model includes race, starting location (home, inpatient ward, intensive care unit or emergency center), Charlson Comorbidity Index, emergency status, ASA-PS classification, procedure, surgical Apgar score, destination after surgery (hospital ward location) and delayed intensive care unit admit within six days. We computed and compared the model mortality prediction ability (C-statistic) as we accumulated features over time.Results
We were able to predict 30 (90)-day mortality with C-statistics from 0.70 (0.71) initially to 0.87 (0.84) within six days postoperatively.Conclusion
We achieved a high level of model discrimination. The CPM enables a continuous cumulative assessment of the patient's mortality risk, which could then be used as a decision support aid regarding patient care and treatment, potentially resulting in improved outcomes, decreased costs and more informed decisions.
SUBMITTER: Myers RB
PROVIDER: S-EPMC8496410 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Postoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality. METHODS:We constructed a multipath convolutional neural network model using patient characteristics, co-morbid conditions, preoperative laboratory values, and intraoperative numerical data from patients undergoing surgery with tracheal intubation at a single medical centre. Data for 60 min prior to a randomly selected time point were utilised. Model performance was compared with a deep neural network, a random forest, a support vector machine, and a logistic regression using predetermined summary statistics of intraoperative data. RESULTS:Of 95 907 patients, 941 (1%) died within 30 days. The multipath convolutional neural network predicted postoperative 30-day mortality with an area under the receiver operating characteristic curve of 0.867 (95% confidence interval [CI]: 0.835-0.899). This was higher than that for the deep neural network (0.825; 95% CI: 0.790-0.860), random forest (0.848; 95% CI: 0.815-0.882), support vector machine (0.836; 95% CI: 0.802-870), and logistic regression (0.837; 95% CI: 0.803-0.871). CONCLUSIONS:A deep-learning time-series model improves prediction compared with models with simple summaries of intraoperative data. We have created a model that can be used in real time to detect dynamic changes in a patient's risk for postoperative mortality.
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Project description:BackgroundPostoperative mortality after colorectal cancer surgery varies across hospitals and countries. The aim of this study was to test the Association of Coloproctologists of Great Britain and Ireland (ACPGBI) models as predictors of 30-day mortality in an Australian cohort.MethodsData from patients who underwent surgery in six hospitals between 1996 and 2015 (CRC data set) were reviewed to test ACPGBI models, and patients from 79 hospitals in the Bi-National Colorectal Cancer Audit between 2007 and 2016 (BCCA data set) were analysed to validate model performance. Recalibrated models based on ACPGBI risk models were developed, tested and validated on a data set of Australasian patients.ResultsOf 18 752 patients observed during the study, 6727 (CRC data set) and 3814 (BCCA data set) were analysed. The 30-day mortality rate was 1·1 and 3·5 per cent in the CRC and BCCA data sets respectively. Both the original and revised ACPGBI models overestimated 30-day mortality for the CRC data set (observed to expected (O/E) ratio 0·17 and 0·21 respectively). Their ability to correctly predict mortality risk was poor (P < 0·001, Hosmer-Lemeshow test); however, the area under the curve for both models was 0·88 (95 per cent c.i. 0·85 to 0·92) showing good discriminatory power to classify 30-day mortality. The recalibrated original model performed well for calibration and discrimination, whereas the recalibrated revised model performed well for discrimination but not for calibration. Risk prediction was good for both recalibrated models. On external validation using the BCCA data set, the recalibrated models underestimated mortality risk (O/E ratio 3·06 and 2·98 respectively), whereas both original and revised ACPGBI models overestimated the risk (O/E ratio 0·48 and 0·69). All models showed similar good discrimination.ConclusionThe original and revised ACPGBI models overpredicted risk of 30-day mortality. The new Australasian calibrated ACPGBI model needs to be tested further in clinical practice.
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Project description:PurposeHospital readmissions in the first weeks following surgery are common, expensive, and associated with increased mortality among colorectal cancer patients. This study is designed to assess the 30-day hospital readmission after colorectal cancer surgery and evaluate the risk factors that affect hospital readmission.MethodsThe study uses data from the Ministry of National Guard-Health Affairs Cancer Registry. All colorectal cancer patients who underwent colorectal cancer surgery between January 1, 2016, and November 31, 2021, were investigated. Factors examined were age, gender, marital status, Body Mass Index, Charlson Comorbidity Index, chemotherapy, radiotherapy, tumor stage, grade, site, surgical approach, length of stay, and discharge location. Kaplan-Meier curves were constructed to assess survival rates between readmitted and non-readmitted patients, and logistic regressions were performed to assess predictors of readmission.ResultsA total of 356 patients underwent tumor resection and 49 patients were readmitted within 30-day of index discharge. The most common reasons for hospital readmissions were gastrointestinal (22.45%), urinary tract infection (16.33%), and surgical site infection (12.24%). In the multivariable analysis, females were 89% more likely to be readmitted compared to males (odds ratio 1.89, 95% confidence intervals 1.00-3.58). Patients with distant metastatic tumors have higher odds of readmission (odds ratio 4.52, 95% confidence intervals 1.39-14.71) compared to patients with localized disease.ConclusionsColorectal cancer readmission is more common in patients with metastatic disease. Strategies to reduce readmission include planned transition to outpatient care, especially among patients with a high risk of readmission.
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Project description:ImportanceQuality improvement programs for colorectal cancer surgery have been introduced with benchmarking based on quality indicators, such as mortality. Detailed (pre)operative characteristics may offer relevant information for proper case-mix correction.ObjectiveTo investigate the added value of machine learning to predict quality indicators for colorectal cancer surgery and identify previously unrecognized predictors of 30-day mortality based on a large, nationwide colorectal cancer registry that collected extensive data on comorbidities.Design, setting, and participantsAll patients who underwent resection for primary colorectal cancer registered in the Dutch ColoRectal Audit between January 1, 2011, and December 31, 2016, were included. Multiple machine learning models (multivariable logistic regression, elastic net regression, support vector machine, random forest, and gradient boosting) were made to predict quality indicators. Model performance was compared with conventionally used scores. Risk factors were identified by logistic regression analyses and Shapley additive explanations (ie, SHAP values). Statistical analysis was performed between March 1 and September 30, 2020.Main outcomes and measuresThe primary outcome of this cohort study was 30-day mortality. Prediction models were trained on a training set by performing 5-fold cross-validation, and outcomes were measured by the area under the receiver operating characteristic curve on the test set. Machine learning was further used to identify risk factors, measured by odds ratios and SHAP values.ResultsThis cohort study included 62 501 records, most patients were male (35 116 [56.2%]), were aged 61 to 80 years (41 560 [66.5%]), and had an American Society of Anesthesiology score of II (35 679 [57.1%]). A 30-day mortality rate of 2.7% (n = 1693) was found. The area under the curve of the best machine learning model for 30-day mortality (0.82; 95% CI, 0.79-0.85) was significantly higher than the American Society of Anesthesiology score (0.74; 95% CI, 0.71-0.77; P < .001), Charlson Comorbidity Index (0.66; 95% CI, 0.63-0.70; P < .001), and preoperative score to predict postoperative mortality (0.73; 95% CI, 0.70-0.77; P < .001). Hypertension, myocardial infarction, chronic obstructive pulmonary disease, and asthma were comorbidities with a high risk for increased mortality. Machine learning identified specific risk factors for a complicated course, intensive care unit admission, prolonged hospital stay, and readmission. Laparoscopic surgery was associated with a decreased risk for all adverse outcomes.Conclusions and relevanceThis study found that machine learning methods outperformed conventional scores to predict 30-day mortality after colorectal cancer surgery, identified specific patient groups at risk for adverse outcomes, and provided directions to optimize benchmarking in clinical audits.
| S-EPMC8076964 | biostudies-literature
Project description:The primary aim of this prospective study is to develop and validate a new prognostic model for predicting the risk of mortality in Emergency Department (ED) patients. The study involved 1765 patients in the development cohort and 1728 in the validation cohort. The main outcome was mortality up to 30 days after admission. Potential risk factors included clinical characteristics, vital signs, and routine haematological and biochemistry tests. The Bayesian Model Averaging method within the Cox's regression model was used to identify independent risk factors for mortality. In the development cohort, the incidence of 30-day mortality was 9.8%, and the following factors were associated with a greater risk of mortality: male gender, increased respiratory rate and serum urea, decreased peripheral oxygen saturation and serum albumin, lower Glasgow Coma Score, and admission to intensive care unit. The area under the receiver operating characteristic curve for the model with the listed factors was 0.871 (95% CI, 0.844-0.898) in the development cohort and 0.783 (95% CI, 0.743-0.823) in the validation cohort. Calibration analysis found a close agreement between predicted and observed mortality risk. We conclude that the risk of mortality among ED patients could be accurately predicted by using common clinical signs and biochemical tests.
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Project description:PurposeThe 30-day mortality rate after hip fracture surgery has been considered as an indirect indicator of the quality of care. The aim of this work is to analyse preoperative and postoperative factors potentially related to early 30-day mortality in patients over 65 undergoing hip fracture surgery.MethodsProspective cohort study including all consecutive primary hip fracture patients over 65 admitted to Trauma and Orthopaedics department from January 1, 2018 to December 31, 2019. Bed-ridden, non- surgically treated patients, and high energy trauma or tumoral aetiology fractures were excluded. A total of 943 patients were eligible (attrition rate: 2.1%). Follow-up included 30-days after discharge. We noted the 30-day mortality after hip fracture surgery, analysing 130 potentially related variables including biodemographic, fracture-related, preoperative, and postoperative clinical factors. Qualitative variables were assessed by χ2, and quantitative variables by non-parametric tests. Odds ratio determined by binary logistic regression. We selected preventable candidate variables for multivariate risk assessment by logistic regression.ResultsA total of 923 patients were enrolled (mean age 86.22±6.8, 72.9% women). The 30-day mortality rate was 6.0%. We noted significant increased mortality on men (OR = 2.381[1.371-4.136], p = 0.002), ageing patients (ORyear = 1.073[1.025-1.122], p = 0.002), and longer time to surgery (ORday = 1.183[1.039-1146], p<0.001), on other 20 preoperative clinical variables, like lymphopenia (lymphocyte count <103/μl, OR = 1.842[1.063-3.191], p = 0.029), hypoalbuminemia (≤3.5g/dl, OR = 2.474[1.316-4.643], p = 0.005), and oral anticoagulant intake (OR = 2.499[1.415-4.415], p = 0.002), and on 25 postoperative clinical variables, like arrhythmia (OR = 13.937[6.263-31.017], p<0.001), respiratory insufficiency (OR = 7.002[3.947-12.419], p<0.001), hyperkalaemia (OR = 10.378[3.909-27.555], p<0.001), nutritional supply requirement (OR = 3.576[1.894-6.752], p = 0.021), or early arthroplasty dislocation (OR = 6.557[1.206-35.640], p = 0.029). We developed a predictive model for early mortality after hip fracture surgery based on postoperative factors with 96.0% sensitivity and 60.7% specificity (AUC = 0.863).ConclusionWe revealed that not only preoperative, but also postoperative factors have a great impact after hip fracture surgery. The influence of post-operative factors on 30-day mortality has a logical basis, albeit so far they have not been identified or quantified before. Our results provide an advantageous picture of the 30-day mortality after hip fracture surgery.
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Project description:BackgroundThe current risk model for percutaneous coronary intervention (PCI) in the UK is based on outcomes of patients treated in a different era of interventional cardiology. This study aimed to create a new model, based on a contemporary cohort of PCI treated patients, which would: predict 30 day mortality; provide good discrimination; and be well calibrated across a broad risk-spectrum.Methods and resultsThe model was derived from a training dataset of 336,433 PCI cases carried out between 2007 and 2011 in England and Wales, with 30 day mortality provided by record linkage. Candidate variables were selected on the basis of clinical consensus and data quality. Procedures in 2012 were used to perform temporal validation of the model. The strongest predictors of 30-day mortality were: cardiogenic shock; dialysis; and the indication for PCI and the degree of urgency with which it was performed. The model had an area under the receiver operator characteristic curve of 0.85 on the training data and 0.86 on validation. Calibration plots indicated a good model fit on development which was maintained on validation.ConclusionWe have created a contemporary model for PCI that encompasses a range of clinical risk, from stable elective PCI to emergency primary PCI and cardiogenic shock. The model is easy to apply and based on data reported in national registries. It has a high degree of discrimination and is well calibrated across the risk spectrum. The examination of key outcomes in PCI audit can be improved with this risk-adjusted model.
| S-EPMC4819905 | biostudies-literature
Project description:Sepsis represents a significant public health burden, costing the NHS £2.5 billion annually, with 35% mortality in 2006. The aim of this exploratory study was to investigate risk factors predictive of 30-day mortality amongst patients with sepsis in Nottingham. Data were collected prospectively from adult patients with sepsis in Nottingham University Hospitals NHS Trust as part of an on-going quality improvement project between November 2011 and March 2014. Patients admitted to critical care with the diagnosis of sepsis were included in the study. In all, 97 separate variables were investigated for their association with 30-day mortality. Variables included patient demographics, symptoms of systemic inflammatory response syndrome, organ dysfunction or tissue hypoperfusion, locations of early care, source of sepsis and time to interventions. A total of 455 patients were included in the study. Increased age (adjOR?=?1.05 95%CI?=?1.03-1.07 p?<?0.001), thrombocytopenia (adjOR?=?3.10 95%CI?=?1.23-7.82 p?=?0.016), hospital-acquired sepsis (adjOR?=?3.34 95%CI?=?1.78-6.27 p?<?0.001), increased lactate concentration (adjOR?=?1.16 95%CI?=?1.06-1.27 p?=?0.001), remaining hypotensive after vasopressors (adjOR?=?3.89 95%CI?=?1.26-11.95 p?=?0.02) and mottling (adjOR?=?3.80 95%CI?=?1.06-13.55 p?=?0.04) increased 30-day mortality odds. Conversely, fever (adjOR?=?0.46 95%CI?=?0.28-0.75 p?=?0.002), fluid refractory hypotension (adjOR?=?0.29 95%CI?=?0.10-0.87 p?=?0.027) and being diagnosed in surgical wards (adjOR?=?0.35 95%CI?=?0.15-0.81 p?=?0.015) were protective. Treatment timeliness were not significant factors. Several important predictors of 30-day mortality were found by this research. Retrospective analysis of our sepsis data has revealed mortality predictors that appear to be more patient-related than intervention-specific. With this information, care can be improved for those identified most at risk of death.
| S-EPMC6259088 | biostudies-other
Project description:ObjectiveThe potential risk of postoperative morbidity is important for gynecologic cancer patients because it leads to delays in adjunctive therapy and additional costs. We aimed to develop a preoperative nomogram to predict 30-day morbidity after gynecological cancer surgery.MethodsBetween 2005 and 2015, 533 consecutive patients with elective gynecological cancer surgery in our center were reviewed. Of those patients, 373 and 160 patients were assigned to the model development or validation cohort, respectively. To investigate independent predictors of 30-day morbidity, a multivariate Cox regression model with backward stepwise elimination was utilized. A nomogram based on this Cox model was developed and externally validated. Its performance was assessed using the concordance index and a calibration curve.ResultsNinety-seven (18.2%) patients had at least one postoperative complication within 30 days after surgery. After bootstrap resampling, the final model indicated age, operating time, and serum albumin level as statistically significant predictors of postoperative morbidity. The bootstrap-corrected concordance index of the nomogram incorporating these three predictors was 0.656 (95% CI, 0.608-0.723). In the validation cohort, the nomogram showed fair discrimination [concordance index: 0.674 (95% CI = 0.619-0.732] and good calibration (P = 0.614; Hosmer-Lemeshow test).ConclusionThe 30-day morbidity after gynecologic cancer surgery could be predicted according to age, operation time, and serum albumin level. After further validation using an independent dataset, the constructed nomogram could be valuable for predicting operative risk in individual patients.
| S-EPMC5453555 | biostudies-literature
Project description:OBJECTIVE:Cardiac surgery ICU characteristics and clinician staffing patterns have not been well characterized. We sought to describe Pennsylvania cardiac ICUs and to determine whether ICU characteristics are associated with mortality in the 30 days after cardiac surgery. DESIGN:From 2012 to 2013, we conducted a survey of cardiac surgery ICUs in Pennsylvania to assess ICU structure, care practices, and clinician staffing patterns. ICU data were linked to an administrative database of cardiac surgery patient discharges. We used logistic regression to measure the association between ICU variables and death in 30 days. SETTING:Cardiac surgery ICUs in Pennsylvania. PATIENTS:Patients having coronary artery bypass grafting and/or cardiac valve repair or replacement from 2009 to 2011. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:Of the 57 cardiac surgical ICUs in Pennsylvania, 43 (75.4%) responded to the facility survey. Rounds included respiratory therapists in 26 of 43 (60.5%) and pharmacists in 23 of 43 (53.5%). Eleven of 41 (26.8%) reported that at least 2/3 of their nurses had a bachelor's degree in nursing. Advanced practice providers were present in most of the ICUs (37/43; 86.0%) but residents (8/42; 18.6%) and fellows (7/43; 16.3%) were not. Daytime intensivists were present in 21 of 43 (48.8%) responding ICUs; eight of 43 (18.6%) had nighttime intensivists. Among 29,449 patients, there was no relationship between mortality and nurse ICU experience, presence of any intensivist, or absence of residents after risk adjustment. To exclude patients who may have undergone transcatheter aortic valve replacement, we conducted a subgroup analysis of patients undergoing only coronary artery bypass grafting, and results were similar. CONCLUSIONS:Pennsylvania cardiac surgery ICUs have variable structures, care practices, and clinician staffing, although none of these are statistically significantly associated with mortality in the 30 days following surgery after adjustment.
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